Initialize ICC demo workspace with LFS tracking + README
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.gitattributes
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# Git LFS tracking for ICC demo workflow.
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# Adds entries here BEFORE committing any new binary format.
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*.sigmf-data filter=lfs diff=lfs merge=lfs -text
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*.sigmf-meta filter=lfs diff=lfs merge=lfs -text
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*.sigmf filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.hdf5 filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.wav filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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README.md
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README.md
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# ICC Demo Workspace
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Recordings, curated datasets, and training workflows for the WavesFM ICC presentation.
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## Workflow
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1. **Upload recordings** — drop `.sigmf-data` + `.sigmf-meta` pairs. Git LFS tracks them automatically via `.gitattributes`.
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2. **Curate** — open the Curator UI, select recordings from this repo, configure slicer + qualifier, produce an HDF5 dataset.
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3. **Commit dataset** — use the Curator's "Commit to Repository" button to land the curated `.h5` back here.
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4. **Train** — open the Model Trainer, select this repo + WavesFM Linear Probe (or LoRA), pick the dataset, submit the run.
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5. **Watch action_run** — the trainer renders `.riahub/workflows/train.yaml` and triggers a runner job. Progress lives under the Actions tab.
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## Notes
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- WavesFM foundation model: `qoherent/wavesfm-base/wavesfm-v1p0.pth` — injected into the workflow automatically.
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- If you upload a binary format not in `.gitattributes`, add it BEFORE the first commit of that file (LFS can't retroactively un-track).
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